Path Planning with Potential Field-Based Obstacle Avoidance in a 3D Environment by an Unmanned Aerial Vehicle

TL;DR

This paper introduces a real-time 3D path planning algorithm for UAVs that enhances obstacle avoidance by modifying the artificial potential field method to address local minima issues, enabling safer and more efficient navigation.

Contribution

A novel rotation-based modification to the artificial potential field algorithm improves obstacle avoidance and local minima handling in UAV 3D path planning.

Findings

Reduces local minima problems in trajectory planning

Generates more efficient collision-avoidance paths

Demonstrates effectiveness in simulation experiments

Abstract

In this paper we address the problem of path planning in an unknown environment with an aerial robot. The main goal is to safely follow the planned trajectory by avoiding obstacles. The proposed approach is suitable for aerial vehicles equipped with 3D sensors, such as LiDARs. It performs obstacle avoidance in real time and on an on-board computer. We present a novel algorithm based on the conventional Artifcial Potential Field (APF) that corrects the planned trajectory to avoid obstacles. To this end, our modifed algorithm uses a rotation-based component to avoid local minima. The smooth trajectory following, achieved with the MPC tracker, allows us to quickly change and re-plan the UAV trajectory. Comparative experiments in simulation have shown that our approach solves local minima problems in trajectory planning and generates more effcient paths to avoid potential collisions with static obstacles compared to the original APF method.